Data Engineer – Commerce & Customer Data

This is a fully remote position, open to applicants in Germany.

📋 Description

• Develop and manage scalable data products alongside ETL and ELT pipelines focused on the Commerce and Customer sectors.

• Structure data from Checkout, Orders, Payments, Customer, Loyalty, and Customer Service.

• Provide data accessibility to Product teams and Analytics.

• Integrate data from various systems and event-driven sources, including via Kafka.

• Create high-performance, traceable, and reusable data models utilizing the modern data stack.

• Maintain data quality, governance, and privacy, particularly regarding personal customer and payment information.

• Collaborate closely with Product Management, Software Engineering, Data, and Business teams.

• Assume responsibility for data products from initial requirements through to production operation.

• Actively participate in the Data Engineering Practice and assist in developing shared standards and methodologies.

⛳️ Requirements

• Several years of experience in Data Engineering or a similar position.

• Strong proficiency in SQL and Python.

• Familiarity with ETL/ELT processes and data modeling.

• Experience with a modern data stack, preferably Snowflake, dbt, and Prefect.

• Knowledge of Kafka or similar event and streaming technologies.

• Understanding of AWS.

• Experience with Infrastructure as Code tools such as Pulumi or Terraform is advantageous.

• Solid grasp of data quality, data governance, and personal data management.

• Proven experience working with cross-functional Product or Engineering teams.

• Independent, organized, and solution-focused work approach.

• Proficiency in German at a minimum of B2 level.

• Fluent in English.

• Nice to have: Experience with e-commerce, Checkout, Order, or Payment data.

• Nice to have: Knowledge of Customer Data, CRM, Loyalty, or Customer Service.

• Nice to have: Experience with Data Mesh and domain-oriented data products.

• Nice to have: Familiarity with BI tools such as Looker, Metabase, or Power BI.

• Nice to have: Experience in building data pipelines for AI or machine learning applications.

🏝️ Benefits

• Flexible working hours.

• Options for remote and mobile work.

• Opportunities for professional development.

• Employee discounts.

• Access to a modern data stack including Snowflake, dbt, Prefect, Kafka, AWS, and Pulumi.

• Direct influence on INTERSPORT’s core Commerce and Customer processes.

• Cross-functional Product teams that foster close collaboration among Data, Engineering, and Product.

• A shared Data Engineering Practice that promotes professional knowledge exchange and established standards.

• Additional benefits.